Extracellular vesicles represent a large heterogeneous class of near and long-distance intercellular communication mediators, released by both prokaryotic and eukaryotic cells. Specifically, the scientific community has shown a growing interest in exosomes, which are nano-sized vesicles with an endosomal origin. Not so long ago, the physiological goal of exosome generation was largely unknown and required more investigation; at first, it was hypothesized that exosomes are able to remove excess, rejects and unnecessary constituents from cells to preserve cellular homeostasis. However, thanks to recent studies, the central role of exosomes in regulating cellular dialogue has emerged. Exosomes act as vectors on cell-cell signaling by their cargo, proteins, lipids, and nucleic acids, and influence physiological and pathological processes. The findings on exosomes are widespread in a large spectrum of biomedical applications from diagnosis and prognosis to thera-pies. In this review, we describe exosome biogenesis and the current methods for their isolation and characterization, emphasizing the role of their cargo in female reproductive processes, from gametogenesis to implantation, and the potential involvement in human female disorders.

Mitochondrial DNA copy number (mtDNAcn) is associated with mitochondrial function contributing various diseases. Understanding of the association between mtDNAcn and infant neurodevelopment, as well as the role of racial disparities, remains unknown. A cohort study was conducted with 55 preterm infants and a single blood sample was collected from each infant during their NICU stay to measure mtDNAcn. NICU Network Neurobehavioral Scale (NNNS) and Bayley Scale of Infant Developmental testing Edition III was assessed during NICU until 2 years of corrected age (CA). Linear regression models were performed to investigate the relationship between the clinical characteristics, neurobehavioral outcomes and mtDNAcn. Majority of our subjects were male, White, non-Hispanic, had C-section, and without preterm premature rupture of membrane (PPROM). Increased mtDNAcn was found associated with younger birth gestational age (GA), nonoptimal neurodevelopment. However, the opposite associations between mtDNAcn and neurodevelopmental outcomes were observed between Black and White infants up to 1 year of CA. Adverse early life experience, together with increased mtDNAcn in White infants, and decreased mtDNAcn in Black infants may be considered as significant positive predictors of poor early life neurodevelopmental outcomes in infants.

53% of adults in the US acknowledge racial bias as a significant issue, 23% of Asian adults experience cultural and ethnic bias, and more than 60% conceal their cultural heritage after racial abuse. AI models like ChatGPT and Google Bard, trained on historically biased data, inadvertently amplify racial and ethnic bias and stereotypes. This paper addresses the issue of racial bias in AI models using scientific, evidence-based analysis and auditing processes to identify biased responses from AI models and develop a mitigation tool. The methodology involves creating a comprehensive database of racially biased questions, terms, and phrases from thousands of legal cases, Wikipedia, and surveys, and then testing them on AI Models and analyzing the responses through sentiment analysis and human evaluation, and eventually creation of an 'AI-BiasAudit,' tool having a racial-ethnic database for social science researchers and AI developers to identify and prevent racial bias in AI models.

3D-printed models with high anatomic fidelity are an increasingly viable tool in simulation-based medical education. One advantage of 3D models is they provide enhanced tactile and spatial understanding of complex anatomy to develop technical skills used in minimally invasive procedures. We propose that 3D anatomical models can improve the development of Interventional Radiology vascular access skills – first described in the 1950s as the Seldinger Technique – for pre-clerkship medical students. Early adoption of 3D-printed technology in pre-clinical medical education can lead to improved student engagement and satisfaction when learning procedural techniques. This study involved creating a 3D model of the upper extremity vasculature from an anonymized CT angiogram, using it as a medical education tool for 31 pre-clinical medical students practicing the Seldinger Technique on a prefabricated venipuncture arm, and assessing student satisfaction with this form of learning. Overall, attendees responded positively to the incorporation of the 3D model in medical education to improve their anatomic understanding and application to the Seldinger Technique. These results indicate that the use of 3D models in simulation-based medical education can provide benefits to acquiring technical skills and the potential to decrease training costs without harming a patient.

Motivation is essential in education, with highly motivated learners engaging more deeply with content and more ably transferring knowledge to new contexts. However, the validity of scales to measure motivation has been underexplored in pre-clerkship medical education. This study evaluates the validity of the Situational Motivation Scale (SIMS) for measuring motivation among pre-clerkship medical students in post-situational and short-term contexts. Using a sample of n = 156 students from the Frank H. Netter MD School of Medicine at Quinnipiac University, SIMS was tested for construct validity with attention paid to content, response process, internal structure, relationships to other variables, and consequences of use evidence. Small modifications from present to past tense in English were made for clarity following focus group feedback, and content validity was ensured via expert consultation. SIMS demonstrated strong internal consistency with a satisfactory Cronbach’s alpha for all subscales and anticipated patterns of correlations. Factor analysis confirmed appropriate factor loadings, with a stronger model fit for the short-term context, and no observed adverse effects on student engagement. These findings support the robustness of SIMS in capturing intrinsic, extrinsic, and amotivation in pre-clerkship medical students, highlighting its applicability for short-term and situational motivational assessment.

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

Background: SARS-CoV-2 Omicron JN.1 has the highest number of amino acid mutations in its receptor binding domain (RBD) and has acquired a hallmark L455S mutation. The immune evasion capability of JN.1 is a subject of scientific investigation. The US CDC used RT-qPCR SGTF as the proxy indicator of JN.1 infections for evaluation of the effectiveness of updated monovalent XBB.1.5 COVID-19 vaccines against JN.1 and recommended that all persons aged ≥6 months should receive an updated COVID-19 vaccine dose. Objective: Recommend Sanger sequencing for diagnosis of JN.1 infections to generate the data based on which guidelines are made to direct vaccination policies. Methods: The RNA in nasopharyngeal swab specimens from patients with clinical respiratory infection was subjected to nested RT-PCR, targeting a 398-base segment of the N-gene and a 445-base segment of the RBD of SARS-CoV-2 for amplification. The DNA sequences were analyzed for amino acid mutations. Results: The N-gene sequence showed Q229K, usually associated with Omicron BA.2.86 (+JN.1). The RBD sequence showed 24 amino acid mutations, including the hallmark L455S mutation for JN.1 and the V483del for BA.2.86 lineage. Conclusions: The CDC should consider Sanger sequencing of the RBD to diagnose JN.1 infections for statistical analysis in making vaccination policies.

Weight-bearing computed tomography (WBCT) enables acquisition of three dimensional bony structure images in a physiological weight-bearing position, which is fundamental in under-standing the pathologic lesions and deformities of the ankle joint. Over the past decade, re-searchers focused on validating and developing WBCT measurements, which has significantly enhanced our knowledge of common foot and ankle disease. Consequently, understanding the application of WBCT in clinical practice is becoming more important to produce improved out-comes in the treatment of disease around the ankle joint. This review will describe an overview of what is currently being evaluated in the foot and ankle surgery using WBCT and where the course of research will be heading in the future.

Introduction Blunt carotid injuries (BCI) in pediatric trauma are quite rare. Due to the low number of cases, only a few reports and studies have been conducted on this topic. This review will discuss how frequent BCI/ BCVI on pediatric patients after blunt trauma is, what routine diagnostics look like, if a CT/ CTA scan on pediatric patients after blunt trauma is always necessary and if there are any negative health effects? Methods This literature review includes reviews, systematic reviews, case reports and original studies in english language between 1999 and 2020 that have dealt with pediatric blunt trauma and the diagnostics of BCI and BCVI. Furthermore, publications on the risk of radiation exposure on children were included in the study. For literature research, Medline (PubMed) and the Cochrane library were used. Results Pediatric BCI/ BCVI, shows an overall incidence between 0.03 – 0.5% of confirmed BCI/ BCVI cases due to pediatric blunt trauma. 1.1 – 3.5% of pediatric blunt trauma patients underwent CTA to detect BCI. Only 0.17 – 1.2% of all CTA scans shows a positive diagnosis for BCI. In children, the median volume CT dose index on a non-contrast head CT is 33 milligray, a computed tomography angiography needs at least 138 mGy. A cumulative doses of about 50 mGy almost triples the risk of leukemia, and doses of about 60 mGy triples the risk of brain cancer. Discussion Knowing that a BCI could have extensive neurologic consequences for children, it is necessary to evaluate routine pediatric diagnostics after blunt trauma. Computed tomography scans (CT) and computed tomography angiography (CTA) are mostly used in routine BCI diagnostics. However, since radiation exposure in children should be as low as reasonably achievable, it should be asked if other diagnostic methods could be used to identify risk groups. Trauma guidelines and clinical scores like the McGovern score are reflect established BCI screening options, as well as using duplex ultrasound.

Lung involvement in rheumatic diseases is a major determinant of patient morbidity and mortality; however, its diagnosis depends on the detection methods used. We compared the incidence of pulmonary involvement in newly diagnosed treatment-naive patients with rheumatic diseases, as assessed by pulmonary function tests (PFTs) and chest high-resolution computed tomography (HRCT). This retrospective study reviewed the symptoms, spirometry results, and HRCT scans of 22 newly diagnosed, treatment-naïve patients and age-matched controls from the University General Hospital “Attikon”, Athens, Greece, between January 2021 and December 2023. Correlations between clinical findings and spirometry parameters were tested. HRCT scans revealed lung abnormalities in all patients, including peribronchial wall thickening (61.5%), ground glass opacities (59.1%), parenchymal bands (54.5%), air-trapping (50%), reticular patterns (45.5%), bronchiectatic changes (40.9%), and parenchymal opacities (31.8%). PFTs, performed in 17/22 (77.3%) patients, revealed definite pathology in 5 patients (restrictive: N=4; obstructive: N=1). No association was found between spirometric indices and clinical parameters. The results indicate a higher prevalence of abnormal chest CT than of PFT abnormalities, suggesting that HRCT is more sensitive in detecting early lung involvement in pediatric patients with rheumatic disease and should be considered in the routine evaluation of these patients.

Penile cancer (PeCa) is a rare urological malignancy characterized by significant geographic variations in both incidence and mortality rates. Due to its rarity and the consequent lack of randomized trials, current management is based on retrospective studies and small prospective trials. In addition, the diagnostic pathways and treatment strategies exhibit substantial heterogeneity, differing significantly between less developed and well-developed countries. The prognosis of PeCa is determined by the presence and extent of regional lymph node (LN) involvement. Therefore, early detection and treatment of LN metastasis is paramount to ensure better outcomes. In recent decades, the overall survival for PeCa has increased, mainly due to advancements in imaging techniques and risk stratification. We aim to provide an overview of the current role of PET CT imaging in the management of patients with PeCa.

Machine learning has gained significant recognition within healthcare organizations as a potent technology for medical image analytics. Among various medical image modalities, Contrast-Enhanced CT (CECT) is widely utilized to acquire additional diagnostic information that can help to better visualize and evaluate certain structures or abnormalities in the human body. Accordingly, there is an increasing demand for machine learning systems for disease analytics using CECT. However, developing such systems presents two significant challenges: high technical complexity and high development effort. This paper presents a software platform that can effectively remedy these challenges. This platform embodies a unified software process for CECT-specific disease diagnosis including oncological diseases, cardiovascular diseases, and gastrointestinal diseases. It integrates multiple types of machine learning models into the disease diagnosis process, where the models can diagnose lesions, malignancies, tumors, medical features of tumors, temporal transitions with a contrast agent, and disease progress. The platform has been implemented in Python using Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a Hepatocellular Carcinoma (HCC) diagnosis system has been implemented.

Computed tomography (CT) is a widely employed imaging modality with diverse clinical applications in pediatric and adult populations, offering numerous advantages. Despite several alternatives to imaging modalities with ionising radiation, CT scans have demonstrated persistent global popularity, contributing to a substantial amount of effective radiation doses for patients. This exposure results in hazards, including stochastic effects that amplify the radiation-induced cancer risk proportionally to the radiation dose received. CT-related radiation comprises a substantial portion of cumulative effective doses within diagnostic radiology, specifically impacting younger patient cohorts undergoing repeated investigations for chronic diseases over time. Scientific literature over recent decades has explored various methods to mitigate radiation doses during medical imaging. One notable strategy involves implementing low-dose and ultralow-dose CT protocols, maintaining diagnostic efficacy in acute and non-acute medical and surgical conditions. Utilising innovative image reconstruction algorithms, such as iterative reconstruction and emerging deep-learning algorithms based on artificial intelligence, plays a pivotal role in achieving an approximately 80% reduction in patient radiation compared to standard doses. This review article discusses the significance of low-dose protocols across a spectrum of acute and chronic medical conditions affecting different body systems.

Abstract: Giant Cell Arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial-GCA and Large Vessel Vasculitis (LV)-GCA, involving the thoracic aor-ta and its branches. 18F-fluorodeoxyglucose positron emission tomography-computed tomogra-phy (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosis of patients with systemic disease. However, its association with soluble inflammatory markers is still elu-sive. This proof-of-concept study aims to identify novel soluble serum biomarkers in PET/CT pos-itive patients with LV-GCA and associate them with active (0 months) and inactive disease (6 months following treatment), in sequential samples. The Most-Disease-Segment Target-to-Background Ratio (TBRMDS) was calculated for 9 LV-GCA patients, while 12 cranial-GCA and 7 Polymyalgia Rheumatica patients with negative initial PET/CT served as disease controls. Serum macrophage-related cytokines were evaluated by Cytometric Bead Array (CBA). Finally, previously published NMR-metabolomics data acquired at the same blood sampling were associated with PET/CT findings. TBRMDS was significantly increased in active versus inactive disease (3.45 vs. 2.55, p=0.008). The analysis identified 6 serum metabolites, as more sensitive to change from the active to inactive state. Among them, choline levels were exclusively altered in the LV-GCA group, but not the disease controls. Cytokine levels were not associated with PET/CT activity. Combining with CRP, ESR, and TBRMDS a composite index was generated to depict well the differences between active and inactive systemic LV-GCA (25.45 vs 11.45, p=0.0039). These preliminary results could pave the way for more extensive studies integrating serum metabolomic parameters with PET/CT imaging data to extract sensitive composite disease indexes useful for the everyday clinical practice.

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

Synchrotron radiation computed tomography (SRCT) allows more accurate calcified plaque and coronary stenosis assessment as a result of its superior spatial resolution, however, typical micro-computed tomography (micro-CT) systems have even higher resolution. The purpose of this study was to compare performance of high-resolution micro-CT with SRCT in the assessment of calcified plaques and a previously published dataset of coronary stenosis assessment. This experimental study involved micro-CT scanning of three-dimensional printed coronary artery models with calcification in situ used in our previously published SRCT study on coronary stenosis assessment. Measurements of coronary stenosis utilizing both modalities were compared using a paired sample t-test. The degrees of stenosis measured on all but one micro-CT dataset were statistically significantly lower than the corresponding SRCT measurements reported in our previous paper (p<0.0005-0.05). This indicates that the superior spatial resolution of micro-CT was able to further reduce over-estimation of stenosis caused by extensive calcification of coronary arteries and hence false positive results. This study shows that high-resolution micro-CT outperforms SRCT in both calcified plaque and coronary stenosis assessment. This finding will become clinically important for cardiovascular event prediction and enable reclassification of individuals with low and intermediate risk into appropriate risk categories when the technical challenges of micro-CT in clinical practice such as the small field of view and demanding on image processing power are addressed.

This study aims to evaluate the patient’s dose exposure from Computed Tomography Pulmonary Angiography (CTPA) examination and to estimates the cancer risk induced from the examinations based on the patient’s size. One hundred patients were recruited, and data information was collected retrospectively. A multi-detector (MDCT) (Philips Brilliance 128, USA) scanner were utilized for the CTPA examination, and dose data were obtained from the system. The effective diameter of each subjects’ image was measured for the Size-specific dose estimates (SSDEs). All subjects divided into Group 1 (19 – 25 cm), Group 2 (25-28 cm) and Group 3 (28-38 cm), where the association between gender were analysed. Effective dose (E), SSDE, organ dose and cancer risk of each group were evaluated and compared statistically using independent t-test and one-way ANOVA. The range of mean CTDI_vol, DLP and E values were (6.44 – 17.42 mGy), (239 – 631 mGy), (5.19 – 13.90 mSv), respectively. In respective with the patient’s effective diameter, the mean SSDE value for Group 1, Group 2 and Group 3 were 9.93 ± 3.89 mGy, 13.70 ± 9.04 mGy and 22.29 ± 7.35 mGy. The organ dose and cancer risk attained for breast, lung and liver were 17.05 ± 10.40 mGy (194 per one million procedure), 17.55 ± 10.86 mGy (192 per one million procedure) and 15.04 ± 9.75 mGy (53 per one million procedure), respectively. Lung and breast with more massive patient’s effective diameter received the highest dose exposure which increases the probability of the cancer risk. CTDI_vol was found to be underestimated, and SSDE provides more accurate in describing the radiation dose and cancer risk. Body effective diameter found to be significant on the estimation except for gender. Therefore, it is essential to apply optimised protocols in order to reduce patient’s exposure during CTPA examination.

Photon-counting Computed Tomography (PCCT) is a promising cardiovascular-imaging technique. Using detectors that count the number and energy of photons in multiple bins, PCCT offers several advantages over conventional CT, including a higher image quality, reduced contrast agent volume, radiation doses, and artifacts. Although PCCT is well established for cardiac imaging in assessing coronary artery disease, its application in vascular imaging remains limited. This review summarizes the available literature and provides an overview of the current use of PCCT for the diagnosis of vascular conditions. Of the 13 records selected in the initial search, 7 articles were included in the present review: 6 cohort studies and 1 case report. This review synthesized the available literature on PCCT in vascular imaging, focusing mainly on endoleaks detection and characterization after endovascular aneurysm repair (EVAR), contrast dose volume, and radiation exposure reduction, particularly in patients with chronic kidney disease and in those requiring follow-up CT.

The objective of this study is to preliminarily evaluate a new CT-biopsy guidance device, an origami needle guide. The device is created by laser cutting the structure from a sheet of cardboard, 3D printing two radiocontrast agent grids on to the surface and folding the structure into a rectangular prism with a viewing window. An abdominal imaging phantom was used to evaluate the device through CT imaging and the targeting of lesions for needle insertion. The lesion targeting trials resulted in a mean targeting error of 1.88 mm with a standard deviation of 0.73 mm. The device attaches to the patient and is rigid enough to adequately support standard biopsy needles, reducing the effect of gravity and the risk of laceration by the needles, making it potentially advantageous for biopsy of superficial lesions and lesions approached from a horizontal orientation. The device supports insertion of multiple needles at once, making it particularly suitable for composite ablation using multiple needles. Another advantage of the device is that it can guide off-axial needle insertion. The low-cost and disposability of the device make it well-suited for the minimally invasive image guided therapy environment.

In the last decade, the role of Computed Tomography Pulmonary Angiography (CTPA) in diagnosing and managing pulmonary embolism (PE) has significantly evolved. Once a purely diagnostic tool, CTPA now plays a crucial role in initial emergency department assessments and treatment planning tailored to the patient’s condition. Advances in CTPA technology, including image quality enhancements and artificial intelligence (AI) applications, necessitate a reassessment of its current utility. This narrative review updates on new CTPA tools and techniques and focuses on data extraction challenges in emergency settings. CTPA studies, often conducted with multidetector scanners, provide essential information for risk stratification and treatment planning. The quantification of thrombotic burden using CTPA is vital for predicting mortality and determining appropriate treatments. Classical scoring systems, such as those developed by Qanadli, Mastora, and Ghanima, convert CTPA findings into quantifiable data to assist clinical decision-making. Despite CTPA not being a standard mortality predictor, significant research suggests including CTPA data, especially RV dysfunction indicators, for prognostic purposes. Recent studies highlight automated techniques for quantifying pulmonary perfusion and thrombus composition, enhancing the accuracy of PE severity assessment. The integration of AI in CTPA, particularly through deep learning algorithms, shows promise in automating thrombus load assessment and improving risk stratification. The continuous development of imaging techniques positions CTPA as a potential tool for comprehensive PE management, aiding in diagnosis, prognosis, and treatment decisions.

Application of toxic antibacterial agents is considered necessary to control prevalent fresh water microorganisms in evaporative cooling water systems, but these agents can adversely affect the environment and human health. Alternatively, natural antibacterial water chemistry has been applied in industrial cooling water systems for over 10 years with excellent results. The tower water chemistry method concentrates natural salts in highly-softened water to produce elevated pH and dissolved solids, with low calcium and magnesium. This practice conserves water while generating only a small volume of non-toxic natural salt concentrate for cost efficient separation and disposal if required. This review presents a novel perspective of natural antimicrobial chemistry for inhibiting parasitic microbiome functional relationships within the bio-triad of Legionella outbreaks, "Trojan Protozoans" and biofilms. The review further examines practical application and function of polyvalent metal ions in the inhibition of biofilms. Reducing global dependence on toxic antibacterial agents discharged to the environment is an emerging concern due to their impact on the natural microbiome, plants, animals and humans. Discharge of antibacterial agents also contributes to development of pathogen resistance. Use of natural antibacterial chemistry can play a key role in managing the cooling water environment in a more ecologically sustainable manner.

Background The acknowledgment of computed tomography (CT) defined diagnosis in high prevalence northern Italy may identify more patients with Coronavirus Disease-2019 (COVID 19) infection, than RT-PCR alone. Methods We retrospectively reviewed 148 chest CT scans of oncological patients who were referred to the Radiological Unit of Policlinico S. Marco from 1st of February 2020 to 30th of April 2020, during the Covid-19 outbreak in Bergamo area. Therefore, we analyzed RT-PCR tests of these 148 patients. Results Among 32 patients with diagnosis of COVID-19 infection: 17 patients were asymptomatic or had mild symptoms (53.1%), while 15 developed severe disease (46.8%). The incidence of COVID-19 infection is 22.9%, the mortality rate is 18.8%. Severe COVID-19 disease is associated with higher median age. We did not find any correlation between disease severity and sex, smoke or cardiovascular comorbidities. Remarkably, patients who were on treatment developed milder disease than cancer patients who were not on treatment. Conclusions The acceptance of CT-defined diagnosis in high prevalence area like Bergamo highlighted a larger number of COVID-19 oncological population than RT-PCR alone, in particular asymptomatic and mild symptomatic patients. We observed that actively treated patients had milder disease, according to previous studies that suggested a protective role of immunosuppression.

Polymyalgia rheumatica (PMR) is an inflammatory disease common in people aged 50 years and older. This condition is characterized by the presence of pain and stiffness involving mainly the shoulder and pelvic girdle. Besides the frequent association with giant cell arteritis (GCA), several conditions may mimic PMR or present with PMR features. Since the diagnosis is basically clinical, an adequate diagnosis of this condition is usually required. Positron emission tomography-computed tomography (PET-CT) has proved to be a useful tool for the diagnosis of PMR. The use of 18F-FDG-PET imaging appears promising as it provides detailed information on inflammatory activity that may not be evident with traditional methods. However, since PET-CT is not strictly necessary for the diagnosis of PMR, clinicians should consider several situations in which this imaging technique can be used in patients with suspected PMR.

The 3D reconstruction of vermiculite-based sealing material was carried out based on CT scanning experiments and Avizo software. The precision and representation of the reconstructed digital core were tested by calculating the pore structure parameters and comparing them with the experimental results of mercury compression. Avizo is a perfect interface with Fluent software to perform micro-scale seepage simulation to explore the distribution of pressure and velocity fields during fluid transport. In this paper, a new approach is proposed to study the penetration leakage of non-metallic gaskets by using equivalent pore networks.

COVID-19 was a terrible worldwide pandemic that caused a global public health crisis, with numerous deaths and a severe economic depression. To suppress the spread of COVID-19 and initiate patient isolation and contact tracing to reduce its effect, early diagnosis was required, with real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test being the most widely used. However, the RT-PCR test was sometimes found to be lengthy and inaccurate and; for that reason, in many cases of severe complications of COVID-19 pneumonia, chest screening with radiography imaging was preferred. In this paper, we carried out a study for COVID-19 detection from computed tomography images, and compared the obtained results using deep learning (DL) and support vector machine (SVM). The findings showed that, despite the excellent results shown by deep learning, the accuracy in predicting COVID-19 using SVM, in the presence of small databases, was slightly higher than DL. The execution time using SVM was also shorter

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, gray matter, and cerebrospinal fluid using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.

In response to a threefold increase in osteoporotic vertebral fractures (OVF) at our institution over the past decade and the subsequent development of new fractures within a year in a third of cases, we evaluated the effectiveness of STIR MRI scans in conjunction with CT scans for initial OVF diagnosis. This study, conducted over five months, involved full spine sagittal STIR MRI scans alongside CT scans for patients over 50 with minor injury-related vertebral fractures. The independent assessments by radiologists and neurosurgeons of 64 patients revealed that while CT scans identified 161 fresh fractures, of which only 68 were previously reported, MRI detected 266 fractures, 1.65 times more than CT. CT scans showed single fractures in 27% of patients and multiple fractures in 73%, whereas MRI identified single fractures in 9% and multiple fractures in 91% of patients. Additionally, MRI demonstrated a 1.5-fold increase in adjacent fracture detection. The use of MRI influenced treatment plans in 67% of cases. Given these findings, we advocate for including a STIR MRI of the entire spine alongside CT scans in OVF cases, as it significantly enhances fracture detection and can alter treatment strategies.

The aim of this study was to investigate succinic acid production from lactose concentrate, a by-product of cheese-making, using Actinobacillus succinogenes and Basfia succiniciproducens. Although the capability of these strains to metabolize different sugars is already known, their application in the conversion of lactose bears high potential for optimization. With regard to B. succiniciproducens this approach is completely novel. In particular the influence of the mediums sugar concentration and its supplementation with yeast extract to prevent a lack of proteins and vitamins were examined. Lactose based media containing sugar concentrations between 20 and 65 g L-1 and 5 g L-1 yeast extract were fermented, whereby both strains showed comparable performances. The best results in succinic acid yield and acid concentration, 0.57 g g-1 initial sugar and 23 g L1, were achieved at an initial sugar concentration of 43 g L-1. The necessity of yeast extract was demonstrated using the sugar optimized medium without supplementation. As a result, yield and concentration of succinic acid dropped to 0.34 g g-1 and 13 g L-1, the sugar consumption decreased from more than 99 to less than 55 %. Therefore the supplementation amount of 5 g L-1 yeast extract can be regarded as well-balanced.

Expressive clinical terminologies are of utmost importance for achieving an semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced querying of SNOMED CT-coded data but also for flexibly augmenting its 350,000 concepts. This ability for postcoordination largely increases the expressivity of the terminology but correlates with an intrinsic complexity. Complicated by the current lack of tooling support, postcoordination is widely either ignored or applied in an error-prone way. To help facilitate the adoption of postcoordination, we implemented a web application that guides through the creation of Postcoordinated expressions (PCEs) while ensuring the adherence to syntactic and semantic constraints. Our approach was largely facilitated by making use of the extensive SNOMED CT specifications as well as advanced HL7 FHIR Terminology Services. Qualitative evaluations confirmed the usability of the developed application and the correctness of PCEs created with it.

A heterograft copolymer with an alginate backbone, hetero-grafted by polymer pendant chains displaying different Lower Critical Solution Temperatures (LCSTs), combined with a pH-responsive Poly(2-vinyl pyridine)-b-poly(ethylene oxide) (P2VP-b-PEO) diblock copolymer forming micellar nanoparticles, was investigated in aqueous media at various pH. Due to its thermoresponsive side chains, the copolymer forms hydrogels with thermo–induced sol–gel transition, above a critical temperature, Tgel (thermo–thickening). However, by lowering pH of the medium in acidic regime, a remarkable increase of the elasticity of the formulation was observed. This effect was more pronounced in low temperatures (below Tgel) suggesting secondary physical crosslinking, which induces significant changes in the hydrogel thermoresponsiveness, transforming the sol–gel transition to soft gel–strong gel. Moreover, the onset of thermothickening shifted to lower temperatures followed by broadening of the transition zone, implying intermolecular interactions between the uncharged alginate backbone with the PNIPAM side chains, likely through H–bonding. The shear–thinning behavior of the soft gel in low temperatures provides injectability, which allows 3D–printing potential applications. Furthermore, the heterograft copolymer/nanoparticles composite hydrogel, encapsulating a model hydrophobic drug in the hydrophobic cores of the nanoparticles, was evaluated as pH-responsive drug delivery system. The presented tunable drug delivery system might be useful for biomedical potential applications.

Pulmonary artery stenosis represents a group of disorders involving main, branch or peripheral pulmonary arteries resulting either in pain, dyspnea, hemoptysis or even no symptoms. Early diagnosis and timely intervention are crucial for reducing mortality, while timely diagnosis is challenging due to the nonspecific symptoms. Computed tomography pulmonary angiography (CTPA) is useful in the diagnosis because it can provide more details about the abnormal changes in the lumen, vessel wall and adjacent mediastinal structures. Congenital and acquired pulmonary artery anomalies have some characteristics on CTPA, which can be useful for differential diagnosis. Awareness of these conditions and is important for the radiologists. This pictorial review provides an overview of CTPA imaging features of pulmonary artery stenosis.

Deep convolutional neural networks (CNN) are favored methods widely used in medical image processing due to their assured shown performance. Recently, the emergence of new lung diseases and the possibility of early detection of their symptoms has attracted many researchers to classify diseases by training deep CNNs on lung CT images. The trained networks are expected to distinguish between lung indications in different diseases, especially at the early stages of them. With the hope of achieving this purpose, we proposed an efficient deep CNN called AFEX-Net with adaptive feature extraction layers that successfully extract distinguishing features and classify chest CT images. The efficiency of the proposed network has two aspects: it is a lightweight network with low number of parameters and fast training and it has adaptive pooling layers and adaptive activation functions to increase its level of compatibility to the input data. The proposed network has been evaluated on a dataset with more than 10K chest CT slices, while an efficient pre-processing method is developed to remove any bias from the images. Additionally, we evaluated the performance of the proposed model on the public COVID-CTset dataset to prove the generalisability of our model. The obtained results confirm the competence of the proposed network in confronting medical images, where prompt and accurate learning is required.

We present progress in fast, high-resolution imaging, material classification, and fault detection using hyperspectral X-ray measurements. Classical X-ray CT approaches rely on data from many projection angles, resulting in long acquisition and reconstruction times. Additionally, conventional CT cannot distinguish between materials with similar densities. However, in additive manufacturing, the majority of materials used are known a priori. This knowledge allows to vastly reduce the data collected and increase the accuracy of fault detection. In this context, we propose an imaging method for non-destructive testing of materials based on the combination of spectral X-ray CT and discrete tomography. We explore the use of spectral X-ray attenuation models and measurements to recover the characteristic functions of materials in heterogeneous media with piece-wise uniform composition. We show by means of numerical simulation that using spectral measurements from a small number of angles, our approach can alleviate the typical deterioration of spatial resolution and the appearance of streaking artifacts.

Obturator herniæ are a quite rare type of abdominal hernia, accounting for less then 1% of all abdominal wall hernias and for 0.2-1.6% of patient with a small bowel obstruction. The obturator canal is the only portion of the obturator foramen that is not obliterated by the membrane of the same name and hosts the obturator neurovascular bundle; it is, for this reason, a locus minoris resistentiae, through which obturator hernias are formed, generally in elderly, multiparous women. Despite the fact that Computed Tomography offers the possibility of a quick, correct preoperative diagnosis, often signs and symptoms of obturator herniæ are quite unspecific; considering that, at the same time, patients with an obturator hernia are generally elderly, with concurrent conditions, this might cause delays in the diagnosis and treatment and produces the high postoperative mortality rates that obturator herniæ yield

Background: Lung CT provides an effective modality to evaluate patients with suspected COVID-19. However, overlapping imaging findings with cardiogenic pulmonary oedema have been reported. Reports comparing lung CT features of these diseases have not been elaborated. Thus, we aimed to investigate these gaps in the knowledge regarding low-dose lung CT features of patients with COVID-19 pneumonia with those with acute heart failure (HF). Methods: This retrospective analysis enrolled hospitalized patients with COVID-19 (n=10) and acute heart failure (n=9) that exclusively underwent low-dose lung CT scans within 24-hours of admission. Clinical and lung CT characteristics were collected and analysed. Results: Ground-glass-opacities (GGO) appearance has been recorded in all subjects in HF and COVID-19 group. There was no significant statistical difference between the two groups for rounded morphology, consolidation, crazy paving pattern, lesion distribution, parenchymal band (P> 0.05). However, diffuse lesions were more frequent in HF cases (55.6% vs. 0%) than in COVID-19 pneumonia, which had predominantly multifocal pattern. Notably, CT images in HF patients were more likely to have signs of interstitial tissue thickening such as the interlobular septums, fissures and peribronchovascular interstitium (55.6% vs 0%, 88.9% vs 20% and 44.4% vs 0%,respectively), as well as cardiomegaly (77.8% vs 0%), increased artery to bronchus ratio (55.6% vs 0%), and pleural effusions (77.8% vs 0%). Conclusions: Major overlaps of lung CT imaging features existed between COVID-19 pneumonia and acute HF cases. However, signs of fluid redistribution are clues that favour HF over COVID-19 pneumonia.

Dual-energy CT (DECT) imaging has broadened the potential of CT imaging by offering multiple postprocessing datasets with a single acquisition at more than one energy level. DECT shows profound capabilities to improve diagnosis based on its superior material differentiation and its quantitative value. However, the potential of dual energy imaging remains relatively untapped, possibly due to its intricate workflow and intrinsic technical limitations of DECT. Knowing the clinical advantages of dual-energy imaging and recognizing its limitations and pitfalls is neces-sary for an appropriate clinical use. The aims of this paper are to review physical and technical basis of DECT acquisition and analysis, to discuss advantages and limitations of DECT in dif-ferent clinical scenarios, to review technical constraints in material labeling and quantification, and to evaluate cutting-edge applications of DECT imaging, including artificial intelligence, qualitative and quantitative imaging biomarkers, and DECT-derived radiomics and radioge-nomics

We present how the Bekenstein-Hawking entropy of a growing black hole variant of Rh = ct cosmology model can be rewritten as a function of the Cosmic Microwave Background (CMB) radiation temperature or Hubble parameter, rather than the Hubble radius, as first pointed out by Tatum and Seshavatharam [1]. We then show how our CMB temperature formulae lead to much higher precision in the estimated entropy of the Hubble radius universe, since the CMB temperature can be measured with great precision. We also briefly discuss how the Schwarzschild metric can be rewritten as a function of the Bekenstein-Hawking entropy, and how the entropy of the universe can be directly linked to recent estimates of the number of quantum operations in the universe since its beginning.

TAFRO syndrome (TS) is a rare variant of idiopathic multicentric Castleman's disease (iMCD) characterized by a confluence of symptoms: thrombocytopenia (T), anasarca (A), fever (F), reticulin myelofibrosis (R), and organomegaly (O). First described in Japan in 2010, the pathogenesis of the disease remains unclear. Due to its heterogeneous presentation and potential life-threatening delays in diagnosis, precise diagnosis is essential. According to the literature, no specific imaging modality has been recommended for the work-up of patients with suspected TS. Here, we report a case of TS and its management using 18F-FDG-PET/CT imaging as an attractive complementary diagnostic tool.

This paper provides a comprehensive overview of the potential applications of dual energy CT (DECT) in improving image quality and diagnostic capabilities of CT arthrography (CTA) in clinical practice. The paper covers the use of virtual non contrast (VNC) images, in which the injected contrast medium is reduced from the articular cavity, in order to better analyze 2D and 3D images of the bone. Moreover , virtual monoenergetic imaging (VMI) applications and their potential use for the reduction of metal artifacts and improving image contrast are reviewed. The role of virtual non calcium (VNCa) in detecting bone marrow edema surrounding the imaged joint will be discussed. Furthermore , the role of iodine map to enhance the contrast between soft tissues, optimizing the visualization of contrast material, and to distinguish contrast material from calcifications is described. Finally, a case series including different joints is provided to underline the additional advantages of high spatial resolution dual energy CT reconstructed images.

Longitudinal image registration of pulmonary computed tomography (PCT) images may serve as an essential tool for investigating the relationship between radiation dose distribution and the occurrence and phenotype of radiation-induced lung disease (RILD). Although numerous longitudinal registration algorithms have been developed for PCT, most similarity-based approaches are not suitable for PCT involving RILD due to the complex tissue variation between two PCT images. Moreover, conventional feature-based approaches might fail to find a sufficient number of matched pairs of feature points due to the disparate lung deformation caused by breathing and RILD. To overcome the challenges resulting from RILD, component structure coherence point drift (CSCPD) was proposed to establish a deformation model by decomposing the chest into several components and matching them with individual parameters based on coherence point drift (CPD). Moreover, a regional vascular point matching (RVPM) algorithm was proposed to generate a vascular subtree and to substantially increase the number of corresponding pairs between two images. Eventually, the components were recomposed and aligned by a thin plate spline algorithm. A performance assessment on 15 pairs of PCT images of patients with RILD yielded recall and precision values of 0.85 and 0.89 for RVPM, respectively. Moreover, the target registration error of CSCPD with RVPM (2.3 ± 1.79) was significantly better than that of conventional CPD with RVPM (2.95 ± 1.89) and conventional CPD (5.04 ± 2.87). Therefore, the proposed registration system is robust enough to address the disparate deformation of lungs with RILD, and it improves registration accuracy within the parenchyma.

This paper presents the latest developments in microCT, both globally and locally, for supporting the additive manufacturing industry. There are a number of recently developed capabilities which are especially relevant to the non-destructive quality inspection of additive manufactured parts; and also for advanced process optimization. These new capabilities are all locally available but not yet utilized to their full potential, most likely due to a lack of knowledge of these capabilities. The aim of this paper is therefore to fill this gap and provide an overview of these latest capabilities, showcasing numerous local examples.

The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.

Deep learning image reconstruction (DLIR) algorithms employs convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and im-proves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently FBP or IR algorithm (Advanced Mod-eled Iterative Reconstruction model-based algorithm [ADMIRE], or Adaptive Iterative Dose Reduction 3D [AIDR 3D]) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs FBP reduced ED (18.45 ± 13.16 mSv vs 22.06 ± 9.55 mSv, P < 0.05), while DLIR vs FBP and vs ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs 14.85 ± 2.73 vs 14.77 ± 32.77 and 11.17 ± 32.77\, P < 0.05) and increased SNR (11.53 ± 9.28 vs 3.99 ± 1.23 vs 5.84 ± 2.74 and 3.58 ± 2.74, P < 0.05). CT scanners employing DLIR reduced radiation ED and im-proved SNR compared to CT scanners using FBP, whereas CT scanners using DLIR improved SNR com-pared to CT scanners using FBP or IR algorithms in ICU patients.

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

In this study, we explored the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. We first utilized the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, we experimented with the Autoencoder algorithm, an unsupervised deep neural network model, which enhanced the detection accuracy to 33.6% in the best case despite some fluctuations. The results demonstrate deep learning models' strong feature extraction capability and adaptability, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. This study underscores the necessity for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The findings provide valuable insights and reference points for future research in financial fraud detection using deep learning methodologies.

Purpose: Prostate cancer is the most common male prevalent malignancy in Europe. Biological recurrence occurs in 20-40% of patients within 10 years following treatment. [18F]F-Choline PET/CT is the most used technique for detecting recurrent disease but is rarely positive for low PSA levels. [18F]PSMA-1007 PET/CT allows early detection of recurrence sites. Methods: We conducted a prospective multicenter study. Each patient had access to a [18F]F-Choline and [18F]PSMA PET/CT. The primary endpoint was to determine the therapeutic impact of the [18F]PSMA comparative to [18F]F-Choline PET/CT. Secondary endpoints were the diagnostic performance and the correlation between PET/CT parameters and prognostic factors of prostate disease. Results: 47 patients were included. A therapeutic modification was observed in 36% of them between what the clinicians expected after the [18F]F-Choline PET/CT and what they finally decided after the [18F]PSMA PET/CT. Diagnostic performance of [18F]F-Choline was 28% versus 70% for [18F]PSMA with a significant difference. With [18F]PSMA PET/CT, there was a significative correlation between PSA level of biological recurrence and SUVmax but not between ISUP score and SUVmax of the selected lesion. Conclusion: This study found a modification in therapeutic management following [18F]PSMA PET/CT in one third of patients. This change could result in earlier and better management with detection of lesions at a lower PSA level than is possible with [18F]F-Choline.

As an implementation of inverse Radon transform, the Second-order Divided-difference Back Projection (SDBP) technique has been proposed in this paper, by which the accurate CT reconstruction can be theoretically realized. A computational model for CT reconstruction filter expressions has been derived on the basis of SDBP technique. The method reveals the correlation between the convolution kernel for data restoration and the filter expression. By substituting kernel functions into the computational model, the amplitude sequences of a variety of filters can be calculated. On the proposed filter construction method, the decomposition form of filters has been discovered, where a series of basic filters have been acquired. The properties of any filter depend on the decomposed basic filters. Basic filters can be used to compose filters in actual needs.

The recent measurement of a cutoff k_min in the fluctuation power spectrum P(k) of the cosmic microwave background may vitiate the possibility that slow-roll inflation can simultaneously solve the horizon problem and account for the formation of structure via the growth of quantum fluctuations in the inflaton field. Instead, we show that k_min may be interpreted more successfully in the R_h=ct cosmology, as the first mode exiting from the Planck scale into the semi-classical Universe shortly after the Big Bang. In so doing, we demonstrate that such a scenario completely avoids the well-known trans-Planckian problem plaguing standard inflationary cosmology, with some additional clues that may motivate further development of Kaluza-Klein cosmologies, string theories and supergravity.

Spectral photon counting cone-beam CT imaging is challenged by individual pixel response behaviour, which leads to noisy projection images and subsequent image artefacts like rings. Existing methods to correct for this either use calibration measurements, like signal-to-thickness calibration (STC), or perform post-processing ring artefact correction of sinogram data or scan reconstructions, without taking the pixel response explicitly into account. Here we present a novel post-processing method (DAC-shifting), which explicitly measures the current pixel response using flat field images, and subsequently corrects the projection data. The DAC-shifting method was evaluated with a repeat series of spectral photon counting imaging (Medipix3) of a phantom with different density inserts, and in iodine K-edge imaging. The method was also compared against PMMA based STC. The DAC-shifting method was shown to be effective in correcting individual pixel response, and was robust against detector instability. On the contrary, STC correction showed varying results, which was almost as good as DAC-shifting when data was acquired just after STC calibration, but worse with larger time differences. In K-edge imaging, DAC-shifting provides a sharper attenuation peak, and more uniform CT values, which is expected to benefit iodine concentration quantification.

During the recent pandemics of COVID-19, sequencing technics became powerful tool for gaining information about the SARS-CoV-2 virus and using this knowledge in our advantage. Thanks to this advantage, scientists over the world were able to search for emerging variations, watching the virus evolving in real time. Assembly of the virus genomes is crucial part of getting this kind of useful information. In our study, we sequenced 79 samples from nasopharyngeal swabs of COVID-19 patients. Positivity on disease was evaluated by qRT-PCR. By studying assembly success rate, we noticed, that Ct-value of qRT-PCR can predict success of genome assembly and SARS-CoV-2 lineage assignment. In this work we described the relationship between Ct-value and further steps of genome construction and its assignment to specific viral strain.

Accurate retroperitoneal lymph node metastasis (LNM) prediction in early-stage testicular germ cell tumours (TGCT) harbours the potential to significantly reduce over- or undertreatment and treatment-related morbidity in this group of young patients as an important survivorship imperative. We investigated the role of computed tomography (CT) radiomics models integrating clinical predictors for the individualized prediction of LNM in early-stage TGCT. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. Dedicated radiomics software was used to segment 273 retroperitoneal lymph nodes and extract features. After feature selection, radiomics-based machine learning models were developed to predict LN metastasis. The robustness of the procedure was controlled by 10-fold cross-validation. Using multivariable logistic regression modelling, we developed three prediction models: A radiomics-only model, a clinical-only model and a combined radiomics-clinical model. The models' performance was evaluated using the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis was performed to estimate the clinical usefulness of the predictive model. The radiomics-only model for predicting lymph node metastasis reached a greater discrimination power than the clinical-only model, with an AUC of 0.84 (± 0.17; 95% CI ) vs 0.60 (± 0.22; 95% CI) in our study cohort. The combined model integrating clinical risk factors and selected radiomics features outperformed the clinical-only and the radiomics-only prediction models and showed good discrimination with an area under the curve of 0.94 ( ± 0.10; 95% CI). The decision curve analysis demonstrated the clinical usefulness of our proposed combined model. The presented combined CT-based radiomics-clinical model represents an exciting non-invasive prediction tool for individualized prediction of LN metastasis in testicular germ cell tumours. Multi-centre validation is required to generate high-quality evidence for its clinical application.

The difficulty in detecting tumors in earlier stages is the major cause of mortalities of patients, despite the advancements in treatment and research regarding ovarian cancer. Deep Learning algorithms are applied to serve the purpose of a diagnostic tool by applying them on CT scan images of the ovarian region. The images go through a series of pre-processing techniques and further the tumor is segmented using the UNet model. Instances are then classified into two categories – benign and malignant tumors. Classification is performed using Deep Learning models like CNN, ResNet, DenseNet, Inception-ResNet, VGG16 and Xception along with Machine Learning models such as Random Forest, Gradient Boosting, AdaBoosting, XGBoosting. DenseNet 121 emerges as the best model on this dataset even after applying optimization on the Machine Learning models by obtaining an accuracy of 95.7%. The current work demonstrates the comparison of multiple CNN architectures among themselves and with common Machine Learning algorithms, with and without optimization techniques applied.

Based upon Maxwell's equations, it has long been established that oscillating electromagnetic (EM) fields incident upon a metal surface decay exponentially inside the conductor, leading to a virtual EM vacuum at sufficient depths. Magnetic resonance imaging (MRI) utilizes radiofrequency (r.f.) EM fields to produce images. Here we present the first visualization of a virtual EM vacuum inside a bulk metal strip by MRI, amongst several novel findings. We uncover unexpected MRI intensity patterns arising from two orthogonal pairs of faces of a metal strip, and derive formulae for their intensity ratios, revealing differing effective elemental volumes (voxels) underneath these faces. Further, we furnish chemical shift imaging (CSI) results that discriminate different faces (surfaces) of a metal block according to their distinct nuclear magnetic resonance (NMR) chemical shifts, which holds much promise for monitoring surface chemical reactions noninvasively. Bulk metals are ubiquitous, and MRI is a premier noninvasive diagnostic tool. Combining the two, the emerging field of bulk metal MRI can be expected to grow in importance. The fundamental nature of results presented here may impact bulk metal MRI and CSI across many fields.

Transcutaneous injection laryngoplasty is a well-known procedure for treating paralyzed vocal fold by injecting augmentation material into it. Hence, vocal fold localization plays a vital role in the preoperative planning as the fold location is required to determine the optimal injection route. In this communication, we propose a mirror environment based reinforcement learning (RL) algorithm for localizing the right and left vocal folds in preoperative neck CT. RL-based methods commonly showed noteworthy outcome in general anatomic landmark localization problem in the recent years. However, such methods suggest training individual agent for localizing each fold, though the right and left vocal folds are located in close proximity and have high feature-similarity. Utilizing the lateral symmetry between the right and left vocal folds, the proposed mirror environment allows for a single agent for localizing both the folds by treating the left fold as a flipped version of the right fold. Thus, localization of both folds can be trained using a single training session which utilizes the inter-fold correlation and avoids redundant feature learning. Experiment with 120 CT volumes showed improved localization performance and training efficiency of the proposed method compared with the standard RL method.

In this study, a new method for synthesizing Ag-NaYF4:Yb3+/Er3+ @ SiO2 nanocomposites was introduced. Using a hydrothermal method, the synthesized Yb3+- and Er3+-codoped NaYF4 upconversion luminescent materials and Ag nanoparticles were doped into upconversion nanomaterials and coated with SiO2 up-conversion nanomaterials. This material is known as Ag-UCNPs-SiO2’ it improves both the luminous intensity because of the doped Ag nanoparticles and has low cytotoxicity because of the SiO2 coating. The morphology of UCNPs was observed using scanning electron microscopy (SEM), and the mapping confirmed the successful doping of Ag nanoparticles. Successful coating of SiO2 was confirmed using transmission electron microscopy (TEM). Fluorescence spectra were used to compare changes in luminescence intensity before and after doping Ag nanoparticles. The reason for the increase in luminescence intensity after doping with Ag nanoparticles was simulated using first-principles calculations. The cytotoxicity of Ag-UCNPs-SiO2 was tested via the cell counting kit-8 (CCK-8) method, and its imaging ability was characterized using the micro-CT method.

Advancements in deep learning and availability of medical imaging data have led to use of CNN based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction (RT-PCR) based tests in COVID-19 diagnosis, CT images offer an applicable supplement with its high sensitivity rates. Here, we study classification of COVID-19 pneumonia (CP) and non-COVID-19 pneumonia (NCP) in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory (biLSTM) architectures. Our study achieved high specificity (CP: 98.3%, NCP: 96.2% Healthy: 89.3%) and high sensitivity (CP: 84.0%, NCP: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the CNN predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities (GGO), indicators of COVID-19 pneumonia disease, were captured by our CNN network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

Imaging techniques such as computed tomographies (CT) play a major role in clinical imaging and diagnosis of malignant lesions. In recent years, spectral CT has emerged in the field of computed tomographies, utilizing detailed information from extracted spectral parameters of the specimen. Metal nanoparticle platforms enable effective payload delivery for this technique. Due to the possibility of surface modification, metal nanoparticles are predestined to facilitate molecular tumor targeting. In this work, we demonstrate the feasibility of anti-plasma membrane Heat shock protein 70 functionalized gold nanoparticles (AuNPs) for tumor specific multimodal imaging. Membrane-associated Hsp70 is exclusively presented on the plasma membrane of malignant cells of multiple tumor entities, but not on corresponding normal tissue cells, predestining this target epitope for tumor-selective in vivo targeting. In vitro microscopical analysis revealed the presence of cmHsp70.1-AuNP in the cytosol of tumor cell lines, being internalized via the endosomal-lysosomal pathway. In tumor bearing mice the biodistribution as well as the intratumorally enrichment of AuNP were examined 24h after i.v. application, in vivo. In parallel to spectral CT analysis, histological analysis confirmed the presence of tumor cells. In contrast to control NP, a significant enrichment of cmHsp70.1-AuNPs has been detected selectively in tumors of different preclinical mouse models. Furthermore, the biodistribution of AuNP, following i.v. injection, was analyzed by a machine-learning approach on digitalized slides. In summary, utilizing mHsp70 on tumor cells for guidance of cmHsp70.1 antibody functionalized nanoparticles enables sufficient enrichment and uniform distribution of AuNPs in mHsp70-expressing tumor cells, adequate for various microscopical imaging techniques and spectral-CT-based tumor delineation, in vivo.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. Early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings pose challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we developed a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieved the highest performance with accuracy, sensitivity, specificity, and combined AUC of 0.93±0.04, 0.94±0.04, 0.93±0.08 and 0.97 respectively in a 5-fold cross-validation study with pulmonary sarcoidosis (n=126) and LCa (n=93) cases. The model offers promising potential for improving the diagnosis of pulmonary sarcoidosis. A detailed ablation study showing the impact of CNN+ViT compared to CNN or ViT alone, and CT+radiomics input, compared to CT or radiomics alone, is also presented in this work.

Abstract: The pursuit of youth and attractiveness is a fundamental desire in contemporary society. For many individuals, the appearance of visible signs of aging, such as skin laxity, gravitational changes in facial and neck soft tissues, and loss of harmonious proportions, can be psychologically distressing. The choice of effective methods for addressing these changes depends on their specific characteristics, the patient's anatomical features, and the type of facial aging. The introduction of 3D imaging to determine the hyoid bone level, the volume of soft tissue structures in the cervico-mental area, ptosis of subplatysmal structures, and the volume of submandibular glands, in addition to ultrasound study of the submental projection at the preoperative stage, are key for taking the best decision regarding preoperative planning. This case report aims to illustrate how modern imaging techniques are a fundamental part of the preoperative assessment of the mid and lower third of the face to ensure the most favorable aesthetic outcomes for each patient. Key message: To attain harmonious results in surgical correction of age-related facial and neck changes, it is crucial to consider the patient's anatomical features and the extent of age-related alterations. A comprehensive facial analysis, taking into account the type of facial aging and employing a CT scan and ultrasound examination of the soft tissues in the chin region, facilitates the development of an individualized plan tailored to achieve the patient's desired outcome.

Obtaining a proper position for THA’s components is a crucial aspect for the implant perfor-mance and consequently patient outcomes. Patient specific kinematic seems to be a key factor to reach the optimal implant positioning. The aim of this study was to assess the accuracy and safety of a computed dynamic analysis system which through patient specific guides tries to improve implant positioning and functional orientation according to patient’s spinopelvic mobility and anatomy.A total of 100 consecutive patients were prospectively enrolled. All patients received an OPS dynamic hip preoperative planning. A TC scan protocol follow up analysis has done at 6 month after surgery.The mean deviation from the planned acetabular inclination and anteversion was 4.3° e 3.8° respectively. The 98% of cases was within +/- 10° of Lewinnek safe zone both for inclination and anterversion. The height of osteotomy deviated on the average of 1.6mm. The 100% cases were included within 4mm of osteotomy. Patient specific and laser guided instru-mentation is safe and accurately reproduce the dynamic planning in terms of components orien-tation, osteotomy level, leg length and offset.

Objectives: Prostate cancer is well known to express high levels of somatostatin receptors and preliminary data suggests that PET imaging with the somatostatin analog, 68Ga-DOTATATE, may allow for whole body staging of patients with metastatic castration resistant prostate cancer (mCRPC) and neuroendocrine prostate cancer (NePC). This study explores the utility of 68Ga-DOTATATE PET-CT to identify metastatic deposits in men with mCRPC and NePC and prognosticate disease progression. Methods: 68Ga-DOTATATE PET-CT was performed in 17 patients with mCRPC and of those 2/17 had NePC. Semiquantitative analysis with standardized uptake values (SUV) (e.g. SUVmax, SUVmean) was performed for each metastatic lesion and reference background tissues. 68Ga-DOTATATE uptake in metastatic deposits was further classified as: mild (less than liver), moderate (up to liver average), or marked (greater than liver). Serial prostate-specific antigen measurements and patient survival were followed up to 3 years after PET imaging to assess response to standard of care treatment. Results: All patients had at least one metastic lesion with identifiable 68Ga-DOTATATE uptake. Marked 68Ga-DOTATATE uptake was found in 7/17 patients, including both NePC patients, and all were non-responders to systemic therapy and died within the follow up period, with a mean time to death of 8.1 months. 3 patients had mild 68Ga-DOTATATE uptake, and all were responders to systemic therapy and were alive 36 months after 68Ga-DOTATATE imaging. Conclusions: 68Ga-DOTATATE is able to identify mCRPC and NePC metastatic deposits, and lesions with 68Ga-DOTATATE uptake > liver may portend poor outcomes in patients with mCRPC.

With the digital transformation of the logistics industry, smart logistics algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews the development history of traditional logistics technology and discusses the key role of technologies such as the Internet of Things, big data analysis, artificial intelligence, and automation in logistics technology innovation. It focuses on the application of intelligent logistics algorithms in path optimization, intelligent scheduling, data mining and prediction, and intelligent warehousing. To solve the problem of inconsistency between training and testing objectives, this paper proposes DRL4Route, a deep reinforcement learning-based path optimization framework, and designs the DRL4Route-GAE model. Validated through extensive offline experiments and online deployments, the model significantly outperforms existing optimal benchmark methods on real datasets, improving the location deviation squared metrics and the top-three location prediction accuracy metrics. These research results provide important support to further promote the intelligent development of the logistics industry.

Newborn screening (NBS) is hailed as a public health success, but little is known about long-term outcomes following a positive newborn screen. There has been difficulty gathering long-term follow-up (LTFU) data consistently, reliably, and with minimal effort. Six programs developed and tested a core set of minimal LTFU data elements. After an iterative data collection process and the development of a data collection tool, the group agreed on minimal LTFU data elements. The denominator captured all infants with an NBS diagnosis, accounting for children who moved or died prior to the follow-up year. They also agreed on three LTFU outcomes: if the child was still alive, had contact with a specialist, and received appropriate care specific to their diagnosis within the year. The six programs representing NBS public health programs, clinical providers, and research programs provided data across multiple NBS disorders. In 2022, 83.8% (563/672) of the children identified by the LTFU programs were alive and living in the jurisdiction; of those, 92.0% (518/563) saw a specialist, and 87.7% (494/563) received appropriate care. The core LTFU data elements can be applied as a foundation to address the impact of early diagnosis by NBS within and across jurisdictions.

Invasive aspergillosis (IA) represents a common form of fungal infection caused by various species of Aspergillus that most frequently affect immunocompromised patients. Typically, this disease occurs preferentially in high-risk groups including patients infected with the human immunodeficiency virus (HIV), leukemia, autoimmune diseases, and organ transplant patients undergoing medical immunosuppression. Considered the second most common cause of op-portunistic fungal infection in humans after Candida albicans, this pathogen predominantly affects the lungs, but may also spread by hematogenous route to various organs and have a heterogenous presentation. Owing to its high iodine levels, high perfusion, and enclosed capsule, the thyroid gland is considered to have a lower susceptibility to microbial invasion and it is fairly uncommon to find associated infectious nodules. In metabolic imaging, 18F-FDG-PET/CT has become increasingly useful for detecting a wide range of infectious and inflammatory diseases and is already the gold standard for certain indications. According to the literature, no studies of hypermetabolic nodular thyroid aspergillosis on 18F-FDG-PET/CT confirmed on histology have yet been reported. Here, we report the first case of a patient with a heterogeneous presentation of IA and the presence of a hypermetabolic nodule in the thyroid with a surprising result.

Colorectal cancer (CRC) is one of the most common types of cancer worldwide. The KRAS mutation is present in 30-50% of CRC patients. This mutation confers resistance to treatment with anti-EGFR therapy. This article aims at proving that Computer Tomography (CT)-based Radiomics can predict the KRAS mutation in CRC patients. The piece is a retrospective study with 56 CRC patients from the Hospital of Santiago de Compostela, Spain. All patients had a confirmatory pathological analysis of the KRAS status. Radiomics features were obtained from the abdominal contrast enhancement CT (CECT), before applying any treatments. We used several classifiers, including adaboost, neural network, decision tree, support vector machine and random forest, to predict the presence or absence of KRAS mutation. The most reliable prediction was achieved using the adaboost ensemble on clinical patient data, with a kappa and accuracy of 53.7% and 76.8%, respectively. The sensitivity and specific were of 73.3% and 80.8%. Using texture descriptors, the best accuracy and kappa were 73.2% and 46%, respectively, with sensitivity and specificity of 76.7% and 69.2%, showing also a correlation between texture patterns on CT images and KRAS mutation. Radiomics could help manage CRC patients, and in the future, it could have a crucial role in diagnosing CRC patients ahead of invasive methods.

Renal Cell Carcinoma (RCC) is generally represented by low-FDG avidity, and [18F]FDG-PET/CT is not recommended to stage the primary tumor. However, its role to assess metastases is still unclear. The aim of this study was to evaluate the diagnostic accuracy of [18F]FDG-PET/CT to correctly identify RCC lung metastases using histology as standard of truth. Records of 350 patients affected by RCC and with CT evidence of at least one lung nodule, were retrospectively analyzed. Inclusion criteria were: a) histologically proven RCC; b) [18F]FDG-PET/CT performed prior to lung surgery; c) lung surgery with histological analysis of surgical specimens; d) complete follow-up available. A per-lesion analysis was performed, and diagnostic accuracy was reported as sensitivity and specificity, using histology as standard of truth. [18F]FDG-PET/CT semiquantitative parameters (Standardized Uptake Value [SUVmax], Metabolic Tumor Volume [MTV] and Total Lesion Glycolysis [TLG]) were collected for each lesion. Sixty-seven (n=67) patients with a total of 107 lesions were included: lung metastases from RCC were detected in 57/107 of cases, while 50/107 lesions were related to others lung malignancies. Applying a cut-off of SUVmax ≥2, the sensitivity and the specificity of [18F]FDG-PET/CT for detect RCC lung metastases were 33.3% (95% CI: 21.4% - 47.1%) and 26% (95%CI: 14.6% - 40.3%), respectively. The analysis demonstrated sub-optimal diagnostic accuracy of [18F]FDG-PET/CT to discriminate between RCC lung metastases versus other malignancies. However, semiquantitative analysis including also volumetric parameters (MTV and TLG) can support [18F]FDG-PET/CT image interpretation.

A right frontoparietal neoplastic lesion was found in a young-aged patient with analgesic refractory headache. Surgical resection was performed, and the pathologist analysis turned out a cellular ependymoma with signs of anaplasia. In the follow-up, a MRI showed a suspicious lesion, so a [18F]Fluorocholine PET/CT was performed. An increased uptake was described in the right parietal region on the margin of residual cystic lesion. The patient got a complete resection which was confirmed later by MR. In the pathology analysis, a focally congestive cerebral parenchyma with a central histiocytic reaction to foreign body area was described. Foreign body reaction in brain tissue is a very rare immune response that has not been well studied. Haemostatic material has been reported as a possible trigger of this response in other organs and could be detected by [18F]FDG PET/CT. Following the experience of the current case report, [18F]Fluorocholine PET/CT could also show a false positive related to foreign body reaction.

Introduction: Clinicians have been struggling with the optimal diagnostic approach of patients with suspected COVID-19. We evaluated the added value of chest CT over RT-PCR alone. Methods: Consecutive adult patients with suspected COVID-19 presenting to the emergency department (Academic Medical Center, Amsterdam University Medical Centers, the Netherlands) from March 16^th to April 16^th were retrospectively included if they required hospital admission and underwent chest CT and RT-PCR testing for SARS-CoV-2 infection. The CO-RADS classification was used to assess the radiological probability of COVID-19, where a score of 1-2 was considered as negative, 3 as indeterminate, and 4-5 as positive. CT results were stratified by initial RT-PCR results. For patients with a negative RT-PCR but a positive CT, serology or multidisciplinary discussion after clinical follow-up constituted the final diagnosis. Results: 258 patients with suspected COVID-19 were admitted, of which 239 were included because they had both CT and RT-PCR testing upon admission. Overall, 112 patients (46.9%) had a positive initial RT-PCR, and 14 (5.9%) had a positive repeat RT-PCR. Of 127 patients with a negative or indeterminate initial RT-PCR, 38 (29.9% [95%CI 21.3-39.3%]) had a positive CT. Of these, 13 had a positive RT-PCR upon repeat testing, and 5 had positive serology. The remaining 20 patients were assessed in a multidisciplinary consensus meeting, and for 13 it was concluded that COVID-19 was ‘very likely’. Of 112 patients with a positive initial RT-PCR result, CT was positive in 104 (92.9% [95%CI 89.3-97.5%]). Conclusion: In a high-prevalence emergency department setting, chest CT showed high probability of COVID-19 (CO-RADS 4-5) in 29.9% of patients with a negative or indeterminate initial RT-PCR result. As the majority of these patients had proven or ‘very likely’ COVID-19 after follow-up, we believe that CT helps in the identification of patients who should be admitted in isolation.

PET imaging with [¹⁸F]F-DOPA has demonstrated high potential for the evaluation and management of pediatric brain gliomas. Manual extraction of PET parameters is time-consuming, lacks reproducibility, and varies with operator experience. In this study, we tested whether a semi-automated image processing framework could overcome these limitations. Pediatric patients with available static and/or dynamic [¹⁸F]F-DOPA PET studies were evaluated retrospectively. We developed a Python software to automate clinical index calculations, including preprocessing to delineate tumor volumes from structural MRI, accounting for lesions with low [¹⁸F]F-DOPA uptake. 73 subjects with treatment-naïve low and high-grade gliomas, who underwent brain MRI within two weeks of [¹⁸vF]F-DOPA PET, were included and analyzed. Static analysis was conducted on all subjects, while dynamic analysis was performed on 32 patients. For 68 subjects, the Intraclass Correlation Coefficient for T/S between manual and ground truth segmentation was 0.91. Using our tool, ICC improved to 0.94. Our method demonstrated good reproducibility in extracting static tumor-to-striatum ratio (p = 0.357), though significant differences were observed in tumor slope (p < 0.05). No significant differences were found in time-to-peak (p = 0.167) and striatum slope (p = 0.36). Our framework aids in analyzing [¹⁸F]F-DOPA PET images of pediatric brain tumors by automating clinical score extraction, simplifying segmentation and Time Activity Curve extraction, reducing user variability, and enhancing reproducibility.

This study assessed the performance of low-dose CT lung scans across various acquisition parameters and examined the impact of iterative reconstruction (IR) algorithms on image quality for lung cancer screening. The performance of multi-slice CT scanners was assessed by using a Catphan 600 phantom, specifically within the CTP404 test module that contains materials like Teflon, Delrin, Acrylic, Polystyrene, LDPE, PMP, Air, and water. Three protocols were selected and categorized into standard, low dose, and ultra-low dose. The standard protocol featured a tube voltage of 100 kVp for single-detector CT, a pitch of 0.75, detector collimation of 0.60 mm, and a gantry rotation time of 0.33 seconds, with 5 mm slice thickness. Images were reconstructed using standard kernels like B80f and 170f, combined with either FBP or various IR algorithms. Findings indicated that the noise power spectrum (NPS) peak value (HU2mm2) increased with higher IR levels, with FBP showing the highest peak. Additionally, NPS spatial frequency diminished as IR levels rose. Optimizing contrast and spatial resolution for both background and regions of interest involved adjusting the target transfer function (TTF) and contrast-to-noise ratio (CNR), crucial for protocol optimization. Proper adjustment of IR levels is vital, as higher levels can alter image texture and affect NPS spatial frequency. The results of this study showed that the use of IR algorithms in low-dose CT lung scans significantly improved image quality, particularly in terms of noise reduction and spatial resolution. However, the choice of acquisition parameters also played a crucial

Abstract Purpose Despite a considerable amount of literature on dual-energy-CT(DECT) iodine uptake of the head and neck, the physiologic iodine uptake of this region has not been defined yet. This study aims to establish reference values for the iodine uptake of healthy organs to facilitate clinical application. Material and Methods: Consecutive venous DECT scans of head and neck were reviewed, and unremarkable exams were included (n=617). 35 region of interest measurements were performed in 16 anatomical regions. Iodine uptake was compared among different organs/tissues and subgroup analysis was performed (male [n=403] vs female [n=214]; young [n=207] vs middle-aged [n=206] vs old [n=204]; normal weight [n=314] vs overweight [n=196] vs obese [n=107]]). Results: Overall mean iodine uptake values ranged between 0.5 and 9.4mg/ml. Women showed higher iodine concentration in the cervical vessels and higher uptake for the parotid gland, masseter muscle, submandibular glands, sublingual glands, palatine tonsils, tongue body, thyroid gland, and the sternocleidomastoid muscle than men (p0.04). With increasing age, intravascular iodine concentrations increased as well as iodine uptake for cerebellum and thyroid gland, while values for tongue and palatine tonsils were lower compared to younger subjects (p0.03). Iodine concentrations for parotid glands and sternocleidomastoid muscles decreased with higher BMI (p0.004), while normal-weighted patients showed higher iodine values inside the jugular veins, other cervical glands, and tonsils versus patients with higher BMI (p0.04). Conclusion: Physiologic iodine uptake values of cervical organs and tissues show gender-, age-, and BMI-related differences, which should be considered in clinical routine of head and neck DECT.

Breast cancer has become the most diagnosed cancer in women globally with 2.3 million new diagnosis each year. Accurate early staging is essential for improving survival rates with metastatic spread from loco regional to distant metastasis decreasing rates by 50%. Current guidelines do not advice the routine use of Positron Emission Tomography (PET)-Computed Tomography (CT) in staging of early breast cancer in the absence of symptoms. However, there is a growing body of evidence to suggest that PET-CT used in this early stage can benefit the patient by improving staging and as a result treatment and outcome as well as psychological burden without increasing costs to the health service. Ongoing research in PET radiomics and artificial intelligence is showing promising future prospects in its use in diagnosis, staging, prognosticatication and assessing response to treatment in breast cancer. Furthermore ongoing research to address current limitations of PET-CT by improving techniques and tracers is encouraging.

Summarizing radiological characteristics between primary lung adenocarcinoma subtypes and correlate them with FDG uptake on PET-CT are important for further treatment. A PET-CT examination was performed on some of the patients and the values of SUV-max were also correlated with the histological and morphological characteristics of masses in the lungs. Results of this analysis showed that the mean size of AIS-MIA cancer was significantly lower than for all other cancer types, while mean size of the acinar cancer was smaller than in solid type of cancer. Metastases were significantly more frequent in solid adenocarcinoma than in acinar, lepidic and AIS-MIA cancer subtypes. The maximum standardized FDG uptake was significantly lower in AIS-MIA than in all other cancer types, and in acinar compared to solid cancer. Papillary adenocarcinoma had higher odds to develop contralateral lymph node involvement compared to other types. Solid adenocarcinoma was associated with higher odds of having metastases and with higher SUVmax. AIS-MIA was associated with lower odds of one unit increase in tumour size, ipsilateral lymph node involvement. Radiology has a significant role in the diagnosis and monitoring of the disease, and in determining its prognosis, and influence on the decision on the method of treatment.

Objective: to evaluate the feasibility of the intra-operative application of a specimen PET/CT imager in a clinical setting. Materials and methods: this is a pilot analysis performed in three patients who received an intra-operative administration of 68Ga-PSMA-11 (n=2) and 68Ga-DOTA-TOC (n=1), respectively. Patients were administrated with PET radiopharmaceuticals to perform radio-guided surgery with a beta-probe detector during radical prostatectomy for prostate cancer (PCa) and salvage lymphadenectomy for recurrent neuroendocrine tumor (NET) of the ileum, respectively. All procedures have been performed within two ongoing clinical trials in our Institute (NCT05596851 and NCT05448157). Pathologic assessment with immunohistochemistry (PSMA-staining and SSA immunoreactivity) was considered as standard of truth. Specimen images were compared with baseline PET/CT images and histopathological analysis. Results: Patients received 1 MBq/Kg of 68Ga-PSMA-11 (PCa) or 1.2 MBq/Kg of 68Ga-DOTA-TOC (NET) prior to surgery. Specimens were collected, positioned in the dedicated specimen container, and scanned to obtain high resolution PET/CT images. In all cases a perfect match was observed between the findings detected by the specimen imager and histopathology. Overall, the PET spatial resolution was sensibly higher for the specimen images compared to the baseline whole-body PET/CT images. Furthermore, the use of the PET/CT specimen imager did not significantly interfere with any procedures, and the overall length of the surgery was not affected using the PET/CT specimen imager. Finally, the radiation exposure of the operating theater staff was lower than 40 µSv per procedure (range 26 – 40 Sv). Conclusion: the image acquisition of specimens obtained by patients who received intra-surgery injection of 68Ga-PSMA-11 and 68Ga-DOTA-TOC was feasible and reliable also in a live-experience session and has been easily adapted to surgery daily-practice. The high sensitivity, together with the evaluation of intra-lesion tumor heterogeneity, were the most relevant results since the data derived from specimen PET/CT imaging matched perfectly with the histopathological analysis.

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Fatigue-related subchondral bone injury of the third metacarpal/ metatarsal (McIII/ MtIII) bone is a common cause of wastage and welfare concern in racehorses. Better understanding of bone health and strength would improve animal welfare and be of benefit for the racing industry. Porosity index (PI) of bone is an indirect measure of osseous pore size and number and is therefore an interesting indicator of bone strength. MRI of compact bone using traditional methods even with short echo times fails to generate enough signal to assess bone architecture as the water protons are tightly bound. Ultrashort echo time (UTE) sequences aim to increase the amount of signal detected in the equine McIII/MtIII condyle. Cadaver specimens were imaged using a novel dual-echo UTE MRI technique and PI was calculated and validated against quantitative CT-derived bone mineral density (BMD). BMD and PI are inversely correlated in the equine distal Mc/ MtIII with a mean r value of -0.29. The r value is significantly different between fore- and hindlimbs. Further work is needed to assess how correlation patterns behave in different areas of the bone and to evaluate PI in horses with and without clinically relevant stress injury.

Aim To perform a comprehensive analysis of discordances between contrast-enhanced CT (ceCT) and 18F-FDG PET/CT in the evaluation of the extra-cerebral treatment monitoring in patients with stage IV melanoma. Materials and methods We conducted a retrospective monocentric observational study over a 3 years period in patients referred for 18F-FDG PET/CT and ceCT in the framework of therapy monitoring of immune checkpoint or tyrosine kinase inhibitors (ICIs or TKIs) as of January-2017. Imaging reports were analysed by two physicians in consensus. Anatomical site responsible for discordances, as well as induced changes in treatment were noted. Results Eighty patients were included and 195 couples of scans analysed. Overall, discordances occurred in 65 cases (33%). Eighty percent of the discordances (52/65) were due to 18F-FDG PET/CT scans upstaging the patient. Amongst these discordances, 17/52 (33%) led to change in patient’s management, the most frequent being radiotherapy of a progressing site. ceCT represented 13/65 (20%) of discordances and induced changes in patients’ management in 2/13 cases (15%). The more frequent anatomical site involved was subcutaneous for 18F-FDG PET/CT findings and lung or liver for ceCT. Conclusions Treatment monitoring with 18F-FDG

Aim To perform a comprehensive analysis of discordances between contrast-enhanced CT (ceCT) and 18F-FDG PET/CT in the evaluation of the extra-cerebral treatment monitoring in patients with stage IV melanoma. Materials and methods We conducted a retrospective monocentric observational study over a 3 years period in patients referred for 18F-FDG PET/CT and ceCT in the framework of therapy monitoring of immune checkpoint or tyrosine kinase inhibitors (ICIs or TKIs) as of January-2017. Imaging reports were analysed by two physicians in consensus. Anatomical site responsible for discordances, as well as induced changes in treatment were noted. Results Eighty patients were included and 195 couples of scans analysed. Overall, discordances occurred in 65 cases (33%). Eighty percent of the discordances (52/65) were due to 18F-FDG PET/CT scans upstaging the patient. Amongst these discordances, 17/52 (33%) led to change in patient’s management, the most frequent being radiotherapy of a progressing site. ceCT represented 13/65 (20%) of discordances and induced changes in patients’ management in 2/13 cases (15%). The more frequent anatomical site involved was subcutaneous for 18F-FDG PET/CT findings and lung or liver for ceCT. Conclusions Treatment monitoring with 18F-FDG

This study aimed to demonstrate the potential role of Dual Energy CT in assessing Bone Mineral Density (BMD) in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using an HAP(fat) map. Statistical analysis was performed using the Bland-Altman method to assess agreement between DXA and DECT HAP(fat) measurements. Mean BMD, vertebral and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP(fat) values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) it was found for measurements at the femoral neck. In conclusion, we demonstrated the enhanced accuracy of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

Total-body PET/CT scanners provide increased sensitivity, enabling the adjustment of imaging parameters by reducing injected activity or shortening acquisition time. This study aimed to evaluate the limitations of reduced [18F]FDG activity doses on image quality, lesion detectabil-ity, and quantification of lesion uptake in the Biograph Vision Quadra, as well as to assess the benefits of the recently introduced ultra-high sensitivity mode in a clinical setting. A number of 26 patients who underwent [18F]FDG-PET/CT (3.0 MBq/kg, 5 min. scan time) were included in this analysis. PET raw data was rebinned for shorter frame durations to simulate 5 min. scans with lower activities in high sensitivity (HS) and ultra-high sensitivity (UHS) modes. Image quality, noise, and lesion detectability (n=82) were assessed using a 5-point Likert scale. The co-efficient of variation (CoV), signal-to-noise ratio (SNR), tumor-to-background ratio (TBR), and standardized uptake values (SUV) including SUVmean, SUVmax, and SUVpeak were evaluated. Sub-jective image ratings were generally superior in UHS compared to HS mode. At 0.5 MBq/kg, le-sion detectability decreased to 95% (HS) and 98% (UHS). SNR was comparable at 1.0 MBq/kg in HS (5.7±0.6) and 0.5 MBq/kg in UHS (5.5±0.5). With lower doses, there were negligible reductions in SUVmean and SUVpeak, whereas SUVmax increased steadily. Reducing [18F]FDG activity to 1.0 MBq/kg (HS/UHS) in a total-body PET/CT provides diagnostic image quality without statistical-ly significant changes in uptake parameters. UHS mode improves image quality, noise, and le-sion detectability compared to HS mode.

Osteoarthritis (OA) is a prevalent disease and the leading cause of pain, disability, and quality of life deterioration. Our study sought to evaluate the image quality and dose of cone-beam com-puted tomography arthrography (CBCT-A) and compare them to digital radiography (DR) for OA diagnosis. Overall, 32 cases of CBCT-A and DR with OA met inclusion criteria and were prospectively analyzed. The Kellgren and Lawrence classification (KLC) stage, sclerosis, osteo-phytes, erosions, and mean joint width (MJW) were compared between CBCT-A and DR. Image quality was excellent in all CBCT-As, with excellent inter-observer agreement. OA un-der-classification was noticed with DR for MJW (p=0.02), osteophyte detection (<0.0001), and KLC (P<0.0001). The HU values obtained for the CBCT did not correspond to the values for MDCT, with a greater mean deviation obtained with the MDCT HU for MBIR1 than MBIR2. CBCT-A has been found to be more reliable for OA diagnosis than DR as revealed by our results using three-point rating scale for the qualitative image analysis, with higher quality and an ac-ceptable dose. Moreover, the use of this imaging technique permits the preoperative assessment of extremities in OA diagnosis, the upright position and bone microarchitecture analysis being two other advantages of CBCT-A.

Background: Parathyroid imaging techniques are used in patients with biochemically confirmed primary hyperparathyroidism (pHPT) to detect the localization of enlarged parathyroid gland(s) (PTG) and thereby to allow more focused, unilateral or minimally invasive parathyreoidectomy (PTX), instead of classical bilateral neck exploration (BNE). Although the preferred sequence of imaging continues to evolve, ultrasonography (US) is usually the first line modality, followed by 99mTc sestamibi parathyroid scan (SPS). However, both tests may have suboptimal localizing accuracy with considerable rate of non-localized cases. Therefore, more accurate four-dimensional computed tomography scan (4D-CT) has been employed for PTG imaging. Currently, there is a paucity of data evaluating the utility of 4D-CT in low case-load settings. Aim: To evaluate the impact of PTG imaging, using 4D-CT in conjunction with it’s intraoperatively displayed results, on the outcomes of surgical PTX. Our primary outcome was sensitivity of 4D-CT in comparison to US and SPS for preoperative PTG adenoma localization. The secondary outcomes were the need for redo surgery and morbidity. Additionally, the characteristics of surgical PTX, i.e. operative time, number of removed parathyroid glands, and rate of simultaneous thyroid surgery, were assessed. Methods: A single-center retrospective analysis of surgically treated patients with pHPT from 01/2010 to 01/2021 was conducted. Evaluation of impact of the preoperative imaging modalities on the results of surgical treatment was carried out. Results: During the study period 290 PTX were performed, 45 cases were excluded due to surgery for secondary, tertiary or recurrent HPT, or due to use of alternative imaging techniques; the remaining 245 patients were included in the study. US was carried out for PTG imaging in 236 (96.3%), SPS in 93 (38.0%), and 4D-CT in 52 patients (21.2%). The use of 4D-CT was associated with a significantly higher rate of successful localization of enlarged PTG (49 cases, 94.2%) compared to US and SPS (74 cases, 31.4%, and 54 cases, 58.1%, respectively). We distinguished between three groups of patients based on preoperative imaging: 1) PTG lateralization via US or SPS in 106 (43.3%) cases, 2) precise localization of PTG via 4D-CT in 49 (20.0%) patients, and 3) in 90 cases (36.7%) PTG imaging failed to localize enlarged gland. The group of 4D-CT localization had significantly shorter operative time, lower rate of simultaneous thyroid resections, as well as lower rate of removal of ≥2 PTG, compared to the other groups. The 4D-CT imaging was also associated with the lowest perioperative morbidity and with the lowest median PTH in one month follow-up; however, compared to the other groups, these differences were statistically not significant. Implementation of 4D-CT (since 01/2018) decreased the need for redo surgery (from 11.5% to 7.3%), and significantly increased the annual case load of PTX at our institution (from 15.3 to 41.0), compared to the period before 4D-CT diagnostics. Conclusions: 4D-CT imaging enabled to precisely locate almost 95% of enlarged PTG in patients with pHPT. Accurate localization and intraoperatively displayed imaging results are useful guides for surgeons in making PTX a faster and safer procedure in a low volume center.

Introduction: CT scan of the skull in patients with head trauma play an important role in identifying patients' injuries; In moderate to severe brain injury; CT scans are arguably the best way to get information about the intracranial complications of a head injury. Researchers have cited a number of reasons for why doctors are increasingly asking for CT scans of patients. In this study; pathological findings in CT scan of patients with mild cranial trauma and level of consciousness 13-15 are examined. Method: In this study, the treatment process of 445 patients who had referred to the emergency department due to skull trauma was examined as research units; and samples were taken by simple random sampling among patients for whom CT scan of the brain was requested. In this study; the reasons for requesting a CT scan of the skull from the requesting physician were questioned through an interview at the time of request. Results: According to the tables; it can be seen that most of the patients referred to the hospital due to skull trauma for CT scan were men and were discharged in less than the first 24 hours. The age group of 21 to 30 years had the highest and the age group over 70 years had the lowest frequency and the highest percentage of request for CT scan was by doctor and the biggest injury that caused patients with mild cranial trauma to go to hospital was fall. Conclusion Based on the findings of this study, cranial trauma is one of the life-threatening factors that can cause minor or severe injuries Its range varies from superficial injuries to hematoma and intracranial hemorrhage, which is a reason to perform CT scans of patients with skull trauma. In order to carefully examine the extent of the damage to the patient and to avoid possible risks and to take the necessary measures for treatment. On the other hand, the lack of clear protocols as well as the lack of clarity and certainty of physicians in their diagnoses to perform graphs of qualified people has increased the number of unnecessary CT scans

Covid-19 is a lung disease caused by a Coronavirus family virus. Due to its extraordinary prevalence and death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of Coronavirus and the participation of physicians in all countries, Information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient's lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively.

This article offers an overview of congenital and acquired vascular anomalies involving the portal venous system in dogs and cats, as determined by multidetector-row computed tomography angiography. Congenital absence of the portal vein, portal vein hypoplasia, portal vein thrombosis and portal collaterals are described. Portal collaterals are further discussed as high- and low-flow connections, and categorized in hepatic arterioportal malformation, arteriovenous fistula, end-to-side and side-to-side congenital portosystemic shunts, acquired portosystemic shunts, cavoportal and porto-portal collaterals. Knowledge of different portal system anomalies helps understand the underlying physiopathological mechanism and is essential for surgical and interventional approaches.

Vaccine antigens must present the correct conformation of viral fusion glycoproteins to elicit effective immune responses. Virus-like particles (VLPs) serve as promising vaccine platforms because they mimic the membrane-embedded conformations of fusion glycoproteins on native viruses. Here, we employed SARS-CoV-2 VLPs (SMEN) presenting ancestral, Beta, or Omicron spikes to identify the variant that elicits potent and cross-protective immune responses in the highly sensitive K18-hACE2 mouse model. A combined intranasal and intramuscular administration regimen of the SMEN vaccine generated effective immune responses and was predominantly mediated by antibodies with minor contributions from T cells. Immunization with SMEN presenting an ancestral spike resulted in 100, 75, or 0% protection against ancestral, Delta or Beta VOC-induced mortality, respectively, whereas SMEN presenting the most divergent Omicron spike provided only limited protection (50%, 0%, and 25%) against ancestral, Delta, and Beta variants, respectively. By contrast, SMEN with a Beta spike offered 100% protection against the variants used in this study. Thus, the Beta variant not only overcame the immunity produced by other variants, but also elicited diverse and effective immune response. Our findings suggest that leveraging the Beta variant spike protein can enhance SARS-CoV-2 immunity, potentially leading to a more comprehensive vaccine against emerging variants.

Evaluation of the usefulness of [18F]FDG-PET/CT in the short-term follow-up of patients admitted for COVID-19 pneumonia and to explore the association of findings with clinical prognostic markers. Prospective study included 20 patients admitted to hospital with COVID-19 pneumonia between November 2020 and March 2021. Clinical and laboratory test findings were gathered at admission, 48-72h post-admission, and 2-3 months post-discharge. [18F]FDG-PET/CT and respiratory function tests were performed at 2-3 months post-discharge. Lung volumes (TLC), spirometry (FVC, FEV1), lung diffusion capacity for carbon monoxide (DLCO), and respiratory muscle strength were measured. PET/CT images were qualitatively and semi-quantitatively interpreted. Volumetric [18F]FDG-PET/CT results were correlated with laboratory test and respiratory parameters. Eleven [18F]FDG-PET/CT (55%) were positive. The main finding was hypermetabolic lymphade-nopathy in the mediastinum (90.9%). SUVpeak of the target mediastinum lesion was correlated with neutrophil and lymphocyte counts. Eleven (55%) patients had impaired respiratory function, most frequently a reduced DLCO (35%). SUVpeak was positively correlated with %predict-ed-DLCO values. Pulmonary TLG was negatively correlated with %predicted-DLCO and TLC values. In the short-term follow-up of patients hospitalized for COVID-19 pneumonia, [18F]FDG-PET/CT findings revealed significant detectable inflammation in lungs and mediastinal lymph nodes that correlated with pulmonary function impairment in more than half of the patients.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

Background and objectives: As the oblique lateral interbody fusion at L5/S1 (OLIF51) and lateral corridor approach (LCA) have gained popularity, the understanding of precise vascular structure at L5/S1 level is indispensable. The objectives of this study were to investigate the vascular anatomy at L5/S1 level, and to compare the movement of vascular tissue between supine and lateral decubitus position using the intraoperative enhanced CT and MRI. Materials and Methods: A total of 43 patients who underwent either OLIF51 or LCA were investigated with the average age at surgery of 60.4 (37-80) years old. The preoperative MRI was taken to observe axial and sagittal anatomy of vascular position under supine position. The intraoperative vein-enhanced CT was taken just before incision in the right decubitus position, and compared to supine MRI anatomy. The iliolumbar vein appearance and its types were also classified. Results: The average vascular window allowed for OLIF51 was 22.8 mm and 34.1 mm at either L5 caudal endplate or S1 cephalad endplate level, respectively. It allowed for LCA was 14.2 mm and 12.6 mm at either level, respectively. The left common iliac vein moved 3.8 mm and 6.9 mm to right direction at either level from supine to right decubitus position, respectively. The bifurcation moved 6.3 mm to caudal direction from supine to right decubitus. The iliolumbar vein was located at 31 mm lateral from midline, and MRI detection rate was 52%. Conclusions: The precise measurement of vascular anatomy indicated that OLIF51 approach was the standard minimally invasive anterior approach for L5/S1 disc level compared to LCA, however, there were many variations in quantitative anatomy as well as significant vascular movements between supine and right decubitus position. In the clinical setting of OLIF51 and LCA surgeries, the careful preoperative evaluation and intraoperative 3D imaging are recommended for the safe and accurate surgery.

Abstract: Background: Coronary angiography CT scans play a pivotal role in diagnosing cardiovascular diseases, providing crucial information for treatment planning. However, concerns regarding radiation exposure have prompted the need for establishing region-specific diagnostic reference levels (DRLs) to ensure patient safety. This study aims to assess radiation exposure during coronary angiography CT scans in the Northeast Assam population and establish DRLs tailored to this demographic. Methods: A total of 380 patients were referred to the Primus Diagnostic Centre and Heath City Hospital, Guwahati Assam, during the study with coronary artery disturbances. Data on the technical parameters used in CT procedures were taken in 2021-2022. The aim and study's objective was Organ and surface dose to specific radiosensitive organs (Chest) estimation using software imPACT 1.0.4 from National Radiological Protection Board (NRPB) SR250 Monte Carlo dataset. Results: The study population (n = 380) comprised 190 men and 190 women with an average age of 29 to 75 years. The Mean ± SD of BMI and ED are 22.42±1.06 and 21.57±4.27 respectively. The mean DLP is 854.67, and the mean ED is 21.57. The effective doses for males (13- 27) mSv were in females (13-29) mSv. The DRL for Male population was found to be 24.26mSv.cm2 whereas for female population it was 24.69mSv.cm2. Conclusions: This study highlights the necessity of establishing tailored diagnostic reference levels for coronary angiography CT scans in the Northeast Assam population. By doing so, healthcare providers can ensure optimal image quality while minimizing radiation exposure, ultimately enhancing patient safety and quality of care. These findings have implications for radiological practice in the region and contribute to the ongoing efforts to standardize radiation doses in medical imaging procedures.

To compare the quality of low-energy VMIs obtained with three DECT platforms according to phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on two generations of split-filter CTs (SFCT-1st and SFCT-2nd) and on one Dual-source CT (DSCT). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d’) were assessed on VMIs from 40 to 70keV. Highest noise magnitude values were found with SFCT-1st and noise magnitude was higher with DSCT than with SFCT-2nd for 26-cm (10.2±1.3%) and 31-cm (7.0±2.5%), and the opposite for 36-cm (-4.2±2.5%). The highest average NPS spatial frequencies and TTF values at 50% (f50) values were found with DSCT. For all energy levels, f50 values were higher with SFCT-2nd than SFCT-1st for 26-cm (3.2±0.4%) and the opposite for 31-cm (-6.9±0.5%) and 36-cm (-5.6±0.7%). The lowest d’ values were found with SFCT-1st. For all energy levels, d’ values were lower with SFCT-2nd than with DSCT for 26-cm (-6.2±0.7%), similar for 31-cm (-0.3±1.9%) and higher for 36-cm (5.4±2.7%). In conclusion, compared to SFCT-1st, SFCT-2nd exhibited lower noise magnitude and higher detectability values. Compared with DSCT, SFCT-2nd had lower noise magnitude and higher detectability for the 26-cm but the opposite was true for the 36-cm.

Radiotherapy commonly utilizes CBCT for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into HU values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow FOV systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent GAN processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm’s ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Geometrical assessments of human skull have been done based on the anatomical landmarks. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also anthropologically. An automated system with multi-phased deep learning networks to predict three-dimensional coordinate values of cranio-facial landmarks, was developed. From a publicly available database, computed tomography images of craniofacial area were obtained. They were digitally reconstructed into three-dimensional models. Sixteen anatomical landmarks were plotted on each of the models and coordinate values of them were recorded. Three-phased regression deep learning networks were trained respectively with 90 training datasets. For evaluation, 30 testing datasets were employed. Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.88. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

Background: Pancreatic invasive ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer mortality in Japan. The early diagnosis of pancreatic cancer, which will increase the number of patients with resectable tumors, is urgently needed. The purpose of the present study was to examine the earliest signs of pancreatic abnormalities on CT in order to facilitate the diagnosis and treatment of PDAC. Methods: Forty-one patients with pancreatic cancer and their 154 CTs were selected for the present study. We used the images that were acquired prior to the diagnosis and examined the pancreas in these images to observe serial changes in the morphology of the pancreas after selecting CT images in which PDAC was suspected. We also confirmed whether the main pancreatic duct was observed around that area of the pancreas. Four thousand two hundred seventy-seven patients without pancreato-biliary disease with 4630 CTs were selected for the control group. Results: Two pancreas shapes were detected: localized constriction of the pancreatic parenchyma referred to as the K-shaped sign, and localized fatty changes. Twenty-four (58.5%) of 41 patients showed the K-shaped sign. The main pancreatic duct without dilatation was noted around the K-shaped sign in 9 of the 24 patients. Eight of 41 patients (19.5%) showed localized fatty changes. Nine of 41 patients (21.9%) showed no abnormality. In the control group, only seven of 4277 patients (0.16%) showed the K-shaped sign. Conclusions: The K-shaped sign including localized fatty changes is the earliest CT sign that presents with pancreatic abnormalities. The K-shaped sign does not indicate PDAC itself but may predict its future development.

Background: To investigate the preoperative clinical and radiological factors that predict the development of a postoperative intraabdominal abscess (IAA) in patients with acute appendicitis who were treated by laparoscopic appendectomy (LA). Methods: Two hundred sixteen patients with pathologically proven acute appendicitis underwent LA between January 2013 and March 2018 in our department. Of these, 147 patients were diagnosed with complicated appendicitis (CA) (CA group), while the other 69 patients were diagnosed with simple appendicitis (SA) (SA group). We compared the perioperative clinical and radiographic factors between the two groups and investigated the predictive factors of postoperative IAA. Results: Sixteen patients developed postoperative IAA in the CA group, while no patients did in the SA group. The univariate analysis revealed that time from onset to surgery more than 3 days (p = 0.011), the preoperative CT finding of periappendiceal fluid (p = 0.003), abscess (p < 0.001), and free air (p <0.001), operation time more than 120 minutes (p = 0.023) and placement of a drainage tube (p <0.001) were significantly associated with the development of IAA. Multivariate analysis revealed that the preoperative CT finding of free air was independently associated with the development of IAA (p = 0.007, odds ratio = 5.427). Conclusions: IAA was developed predominantly in the patients with CA. Preoperative CT findings of free air was found to be an independent predictor for the development of IAA. Surgeons should be meticulous in managing the postoperative course of patients with this finding.

The gut microbiota (GM) communicates with the brain via biochemical signaling constituting the gut-brain axis, which significantly regulates the body's physiological processes. The GM dysbiosis can impact the digestive system and the functioning of the central nervous system (CNS) linked to the onset of neurodegenerative diseases. In this review, the scientific data compiled from diverse sources primarily emphasizes the neuropathological characteristics linked to the accumulation of modified insoluble proteins (such as β-Amyloid peptides and hyperphosphorylated tau proteins) in Alzheimer’s Disease (AD) and the potential impact of gut microbiota (GM) on AD susceptibility or resilience. The specific GM profile of human beings may serve as an essential tool for preventing or progressing neurodegenerative diseases like AD. This review focuses mainly on the effect of gut microfauna on the gut-brain axis in the onset and progression of AD. The GM produces various bioactive molecules that may serve as proinflammatory or anti-inflammatory signaling, contributing directly or indirectly to the repression or progression of neurodegenerative disorders by modulating the response of the brain axis. Human studies must focus on further understanding the gut-brain axis and venture to clarify microbiota-based therapeutic strategies for AD.

Abstract: Background: Trauma is the leading cause of morbidity and mortality in young patients, acute surgical conditions cause morbidity and mortality in patients of all ages. Computed tomography scan (CT) assist greatly in the investigation and management of these patients with blunt and penetrating trauma, facilitating early diagnosis and expedites early appropriate treatment of these patients. Acute non-traumatic surgical conditions also mimic non-surgical pathologies and differentiating the two is important because management is different and any delay in surgical intervention results in high morbidity and some may even result in mortality. Method: Literature review of the role of multidetector CT scan (MDCT) in managing acute surgical emergencies and trauma-diagnosis in the trauma patient and patients presenting with acute surgical emergencies. The focus is on the role of the MDCT in the management of these patients. Results highlight that the use of MDCT expedites management of these patients, allowing early surgical intervention and preventing unnecessary surgical interventions where the intervention is not indicated, based on the MDCT results. Conclusion: This paper emphasizes the important role that is played by the MDCT in the management of the above patients.

(1) Background: This retrospective study aimed to compare the efficacy and safety of one-stage computed tomography (OSCT)- to that of two-stage computed tomography (TSCT)-guided localization for the surgical removal of small lung nodules. (2) Methods: We collected data from patients with ipsilateral pulmonary nodules who underwent localization before surgical removal at Veteran General Hospital Kaohsiung between October 2017 and January 2022. The patients were divided into the OSCT and TSCT groups. (3) Results: We found that OSCT significantly reduced the localization time and risky time compared to TSCT, and the success rate of localization and incidence of pneumothorax were similar in both groups. However, the time spent under general anesthesia was longer in the OSCT group than in the TSCT group. (4) Conclusions: The OSCT-guided approach to localize pulmonary nodules in hybrid operation room is a safe and effective technique for the surgical removal of small lung nodules.

Severe respiratory syncytial virus (RSV) infections in early life have been linked to the development of chronic airway disease. RSV is a potent inducer of reactive oxygen species (ROS) which contributes to inflammation and enhanced clinical disease. NF-E2-related factor 2 (Nrf2) is an evolutionary conserved redox-responsive protein that helps to protect cells and whole organisms from oxidative stress and injury. The role of Nrf2 in the context of viral-mediated chronic lung injury is not known. Herein, we show that RSV experimental infection of adult Nrf2 deficient BALB/c mice (Nrf2-/-; Nrf2 KO) is characterized by enhanced disease, increased inflammatory cell recruitment to the bronchoalveolar compartment, and a more robust upregulation of innate and inflammatory genes and proteins, compared to wild type Nrf2+/+ competent mice (WT). These events that occur at very early time points, lead to increased peak RSV replication in Nrf2 KO compared to WT mice (day 5). To evaluate longitudinal changes of the lung architecture, mice were scanned weekly by high-resolution micro-computed tomography (micro-CT) imaging up to 28 days after initial viral inoculation. Based on micro-CT qualitative 2D imaging and quantitative reconstructed histogram-based analysis of lung volume and density, we found that RSV infected Nrf2 KO mice developed significantly greater and prolonged fibrosis compared to WT mice. Results of this study underscore the critical role of Nrf2-mediated protection from oxidative injury not only in acute pathogenesis of RSV infection, but also in its long-term consequences of chronic airway injury.

Post chemotherapy retroperitoneal lymph node dissection (PC-RPLND) in non-seminomatous germ-cell tumours (NSTGCTs) is a complex procedure. We evaluated whether 3D computed tomography (CT) rendering and their radiomics analysis help predict resectability by junior surgeons. The ambispective analysis was performed between 2016-2021. Prospective group (A) of 30 patients undergoing CT were segmented using 3D slicer software while retrospective group (B) of 30 patients were evaluated with conventional CT (without 3D reconstruction). CatFisher’s exact test showed a p-value of 0.13 for group A and 1.0 for Group B. Difference between proportion test showed a p-value of 0.009149 (IC 0.1-0.63). Proportion of correct classification showed a p-value of 0.645 (IC 0.55-0.87) for A, and 0.275 (IC 0.11-0.43) for Group B. Furthermore, 13 shape features were extracted: elongation, flatness, volume, sphericity, surface area, among others. Performing logistic regression with the entire dataset, n=60, the results were: Accuracy: 0.7, Precision: 0.65. Using n=30 randomly chosen, the best result obtained was Accuracy: 0.73, Precision: 0.83, with a p-value: 0.025 for Fisher's exact test. In conclusion, the results showed a significant difference in the prediction of resectability with conventional CT versus 3D reconstruction by junior surgeon versus experienced surgeon. Radiomics features used to elaborate an artificial intelligence model improve the prediction of resectability. The proposed model could be of great support in a university hospital, allowing to plan the surgery and to anticipate complications.